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采用神经网络的断路器传动机构磨损预测

刘创 刘宏昭

刘创, 刘宏昭. 采用神经网络的断路器传动机构磨损预测[J]. 机械科学与技术, 2017, 36(6): 869-876. doi: 10.13433/j.cnki.1003-8728.2017.0608
引用本文: 刘创, 刘宏昭. 采用神经网络的断路器传动机构磨损预测[J]. 机械科学与技术, 2017, 36(6): 869-876. doi: 10.13433/j.cnki.1003-8728.2017.0608
Liu Chuang, Liu Hongzhao. Wear Prediction of Circuit Breaker Transmission Mechanism based on Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 869-876. doi: 10.13433/j.cnki.1003-8728.2017.0608
Citation: Liu Chuang, Liu Hongzhao. Wear Prediction of Circuit Breaker Transmission Mechanism based on Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 869-876. doi: 10.13433/j.cnki.1003-8728.2017.0608

采用神经网络的断路器传动机构磨损预测

doi: 10.13433/j.cnki.1003-8728.2017.0608
基金项目: 

国家自然科学基金项目(51275404)资助

详细信息
    作者简介:

    刘创(1990-),硕士研究生,研究方向为机电系统建模与优化技术,Liuchuang_xaut@163.com

    通讯作者:

    刘宏昭(联系人),教授,博士生导师,liu-hongzhao@163.com

Wear Prediction of Circuit Breaker Transmission Mechanism based on Neural Network

  • 摘要: 针对某大型断路器机构系统磨损试验成本高的特点,通过建立磨损预测模型,对其传动机构危险关节的磨损量进行了预测分析。基于2种典型的预测模型建立方法,采用销盘磨损实验数据,分别建立磨损预测模型。对比分析表明Elman网络模型的预测精度较高,可准确的反映磨损率与接触压力、相对滑动速度和材料硬度之间的规律。考虑运动副间隙的存在,基于非线性弹簧阻尼模型,利用ADAMS软件仿真获得传动机构危险关节的动力学参数。基于Hertz接触理论对动力学参数进行变换,并将其作为预测模型的输入信息,对关节的磨损进行预测计算。通过迭代分析,发现随着断路器开断次数的增加,轴套表面一些特定位置的磨损越来越严重。对比采用固定系数下的Archard模型,表明预测模型计算的结果对磨损失效判定更具参考价值。
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出版历程
  • 收稿日期:  2016-01-23
  • 刊出日期:  2017-06-05

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